- MNE — MNE 1. 9. 0 documentation - Aalto
Open-source Python package for exploring, visualizing, and analyzing human neurophysiological data: MEG, EEG, sEEG, ECoG, NIRS, and more Distributed, sparse, mixed-norm, beamformers, dipole fitting, and more Advanced decoding models including time generalization Receptive field estimation with optional smoothness priors
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- mne·PyPI
MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more It includes modules for data input output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, statistics, and more
- Home | MNE-CPP
MNE-CPP is a cross-platform, open-source framework which offers a variety of software tools to the neuroscientific research community We provide applications for the acquisition and processing of MEG EEG data, both in real-time and offline
- GitHub - mne-tools mne-python: MNE: Magnetoencephalography (MEG) and . . .
MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more It includes modules for data input output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, statistics, and more
- MNE-Python — Neural Data Science in Python
MNE-Python is an open-source Python package for working with EEG and MEG data It was originally developed as a Python port (translation from one programming language to another) of a software package called MNE, that was written in the C language by MEG researcher Matti Hämäläinen
- MNELAB
MNELAB is a graphical user interface (GUI) for MNE-Python, a package for EEG MEG analysis Read the tutorial for installation instructions and first steps The examples showcase MNELAB for common use cases
- Tutorials — MNE 1. 9. 0 documentation
These tutorials provide narrative explanations, sample code, and expected output for the most common MNE-Python analysis tasks The emphasis here is on thorough explanations that get you up to speed quickly, at the expense of covering only a limited number of topics
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